Vehicle Data

Data Service Center for electric vehicles: Prof. Fengchun Sun, funder of the National Engineering Laboratory of Electric Vehicles (NELEV) of Beijing Institute of Technology (BIT), led the team to develop the Beijing Service and Management Center for Electric Vehicles (SMC-EV), which has been upgraded to the National Monitoring and Management Platform for New Energy Vehicles (NMMP-NEV) in 2017. As of Jan 20, 2021, more than 4.06 million vehicles have been connected to the NMMP-NEV, with an average monthly connection of nearly 150,000 vehicles and a total mileage of 120.87 billion kilometres. The daily online rate exceeds 65%, and the real-time online rate is nearly 25%.

Role: Safety is one of the major concerns that hinder the mass-adoption and market-penetration of EVs. Several fire incidents that originated from battery systems have been reported by the media, which highlights the urgency to take necessary measures for safety assurance. In addition to perfecting the safety design from the manufacturing viewpoint, it is a viable solution to establish a platform that can monitor the vehicle operation and provide real-time fault prognosis for EVs. In this context, the SMC-EV (as shown in Fig. 1) was built in Beijing, 2011. Its main functions include monitoring and collecting the real-time operating data of EVs, such as voltage and temperature of battery systems, and conducting in-depth analysis and research through big-data techniques. It can also obtain motion information of the monitored vehicles and the states of key components through vehicle-to-platform communication. The connected vehicles mainly consist of public service vehicles, such as taxis, public transit buses, and sanitation vehicles, etc. The vehicle monitoring information of a certain car (already declassified) in SMC-EV is demonstrated in Fig. 2.

Fig.1 The management mechanism of SMC‐EV

Fig. 2 The vehicle monitoring information of a certain car in SMC-EV.

Fig. 3 Interface of the NMNEV Platform

Based on the above platform, a large amount of real-time operating data of EVs has been accumulated, and the National University New Energy Vehicle Big Data Innovation and Entrepreneurship Competition relying on this platform has released some data for the competition and exploratory research.Here are some public dataset list:

Public data list


Data set




Design of New Energy Passenger Vehicle Public Charging Demand Calculation Model



Small sample data of 2020 Shanghai New Energy Vehicle Big Data Competition



Electric Vehicle Driving SOC Prediction Train&Test



Electric Vehicle Mileage Prediction Train&Test



1. J. Hong, Z. Wang, W. Chen and L-Y Wang, "Multi-fault Synergistic Diagnosis of Battery Systems Based on the Modified Multi-scale Entropy for Electric Vehicles", InternationalJournal of Energy Research. 2019, 43:8350–8369. (Download)

2. J. Hong, Z. Wang, W. Chen and Y. Yao, "Synchronous multiparameter prediction of battery systems on electric vehiclesusing long short-term memory networks", Applied Energy, 2019, 254: 113648. (Download)

Adress:No.5 South Zhongguancun St., Haidian District, Beijing,100081,China.   Copyright  ©  2020-   AESA  All Rights Reserved.
Links: Beijing Institute of Technology    Applied Energy    MIT-Ju Li Group    Chinese J. ME    Sch. Mech Engin